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Reseach Article

Prognosis of Heart Disease using Data Mining Techniques: A Comprehensive Survey

by Ankita Naik, Nitesh Naik
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 17
Year of Publication: 2018
Authors: Ankita Naik, Nitesh Naik
10.5120/ijca2018917765

Ankita Naik, Nitesh Naik . Prognosis of Heart Disease using Data Mining Techniques: A Comprehensive Survey. International Journal of Computer Applications. 181, 17 ( Sep 2018), 14-18. DOI=10.5120/ijca2018917765

@article{ 10.5120/ijca2018917765,
author = { Ankita Naik, Nitesh Naik },
title = { Prognosis of Heart Disease using Data Mining Techniques: A Comprehensive Survey },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2018 },
volume = { 181 },
number = { 17 },
month = { Sep },
year = { 2018 },
issn = { 0975-8887 },
pages = { 14-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number17/29913-2018917765/ },
doi = { 10.5120/ijca2018917765 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:06:12.934333+05:30
%A Ankita Naik
%A Nitesh Naik
%T Prognosis of Heart Disease using Data Mining Techniques: A Comprehensive Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 17
%P 14-18
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Prediction and diagnosis of heart disease has become a formidable factor faced by medical practitioners and hospitals both in India and also worldwide. The early and timely diagnosis of heart disease plays a very crucial role in halting its advancement and reducing related medical costs. Taking into account the ever-increasing rise in heart disease-induced mortality, different techniques have been adopted to treat it. The idea intends to develop a heart disease prediction model, which will implement ensemble techniques, can help the doctors in detecting the heart disease status based on the patient's clinical data. This paper provides a quick and facile analysis and understanding of available prediction models using data mining from 2011 to 2017. The comparison shows the accuracy level of each model given by different researchers.

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Index Terms

Computer Science
Information Sciences

Keywords

Prediction heart disease classification ensemble diagnosis